Air pollution is a serious environment problem currently faced by many countries. Air pollutants including PM2.5 not only reduce air visibility but also influence outdoor activities. Moreover, studies have shown that PM2.5 can directly enter lower respiratory tract of a human body, and may be closely related to respiratory diseases and heart diseases. The respiratory diseases currently show a rising trend in China, which attracts much attention.
The most important thing in preventing air pollution is pollution source control. A premise for performing subsequent management is to locate emission sources of the air pollutants, and to determine their emission amounts; such information means a lot to decision makers.
Conventionally, two methods are used to trace sources of the air pollutants. The first is a backward tracing method based on ground sampling. This method performs on-site ground pollutant sampling and then analyzes chemical ingredients of the samples in laboratories. Then sources of the pollutants are determined based on the chemical ingredients of the pollutants. The most typical approach adopting this method is a receptor model, which is also the most widely used method for tracing pollutant sources currently. The other is a forward tracing method based on an emission inventory and a diffusion model. This method puts an acquired emission inventory into a diffusion model to simulate movement of the pollutants. This method is most suitable for pollution prediction. Both the methods have their advantages and disadvantages. For the backward tracing method, a large amount of on-site sampling and laboratory analysis are required, which makes the workload heavy and the cost high, making it difficult to be used in large scale and continuously. Instead, it is only discontinuously used in some very important areas. As for the forward tracing method, the emission inventory plays a very important role. Unfortunately, up till now there is no complete emission inventory available in China. Moreover, unclear classification of the emission sources and lack of emission factors database make it hardly possible to set up an emission inventory in some situations.
With the rapid development of space technologies, the capability and precision of acquiring ground surface spatial information are greatly improved. Satellite remote sensing data is tremendously improved in terms of their temporal resolution, spatial resolution and resolution. It has been proved feasible to use satellite remote sensing images to quantitatively acquire physical and chemical properties of the ground surface or air. With the further improvement of research and improvement of algorithms, the precision of quantitative retrieval is constantly improved. There have been a lot of researches in using remote sensing images to estimate concentration of the air pollutants and to analyze distribution of the air pollutants. A problem to be solved is how to use the periodically acquired satellite remote sensing images obtained at a low cost but having a big space coverage area to address the problem of tracing sources of pollutants.